Is a basic neural network architecture better with small datasets?
I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it.
That way, you can be almost sure you're not underfitting/underperforming due to network capacity.